For ML Platform, MLOps, and Infra Teams
Run more AI experiments per GPU without changing your stack
WoolyAI is a runtime for dynamic sharing of GPU cores and VRAM on NVIDIA GPUs that integrates directly into your existing training and inference stacks.
Most notebooks, experiments, and inference workloads are bursty or only partially use the device. Static GPU allocation leaves expensive compute and VRAM stranded. WoolyAI helps you reclaim that capacity without changing application code.
- No code changes
- Compatible with Pytorch, vLLM, Triton, any CUDA app
- Native Kubernetes Operator
- Integrates with Slurm
- Multi GPU support
Before WoolyAI
Notebook A
1 GPU reserved
Compute
35%
VRAM
48%
⚠️ idle capacity stranded
HPO Trial B
1 GPU reserved
Compute
22%
VRAM
40%
⚠️ idle capacity stranded
Inference C
1 GPU reserved
Compute
67%
VRAM
50%
⚠️ idle capacity stranded
After WoolyAI
Shared GPU Runtime
Higher Density
Notebook A
Active Share
HPO Trial B
Active Share
Inference C
Active Share
LoRA D
Active Share
Total Cores Utilization 100%
Managed VRAM Residency 110%
Weight Dedup
Dynamic Cores
VRAM overcommit
More jobs per GPU • Policy-aware granular sharing
Share GPUs smarter: Runtime Core scheduling, VRAM overcommit, Weight dedup
Pillar 1
Scheduling
(GPU core-level)
Fractional Core Allocation
Priority-Based Core Sharing
Elastic Core Redistribution
Pillar 2
VRAM
Virtualization
Elastic VRAM Overcommit
Max-Density Scheduling
Smart Swap Eviction
Pillar 3
Weights
Dedup
Shared Weights Dedup
Lower VRAM Footprint
Faster cold starts
Pillar 4
CPU-GPU
Decoupling
CPU Pods Accelerated
Transparent GPU Offload
Route-to-Any GPU
Higher Utilization
Faster Queue Times
Balanced Cluster
Placement Flexibility
Benefits
ML Teams - Run more notebooks on the same GPUs without slowing users down
Don’t reserve full GPUs the whole time for bursty Notebooks. WoolyAI reclaims idle compute and memory between bursts, keeping users responsive as GPU utilization rises.
ML teams - Start queued experiments sooner
Don’t let the experiments wait in the queue because the scheduler’s behavior requires an exact VRAM fit before placing them. WoolyAI overcommits GPU memory to start more experiments immediately, then uses smart swapping to keep multiple jobs running safely on the same GPU.
NeoClouds - Keep dedicated inference endpoints responsive without leaving GPUs half idle
Don’t reserve dedicated GPUs for bursty or low-demand model endpoints. WoolyAI will enable multiple hot model endpoints to share the same GPUs, swapping memory intelligently when needed, so providers can keep models ready without incurring full-GPU cost for each one.
NeoClouds - Serve more customer-specific LoRA endpoints on the same GPUs
Don’t waste precious GPU VRAM loading the same base model repeatedly for LoRA variant inference endpoints. WoolyAI keeps a single shared copy of the base weights in memory, so GPU usage scales mainly with adapter state and live runtime state rather than repeated full-model copies.
Capability Comparison
| Queueing / Preemption | Time-slicing / MIG | Budgets / Quotas | WoolyAI (4 pillars) | |
|---|---|---|---|---|
| Concurrent Job Execution | ✕ | ~ | ✕ | ✓ Dynamic core sharing |
| Enforce priority / SLA inside the GPU | ~ | ~ | ~ | ✓ Priority-based core allocation |
| Place more jobs than physical VRAM | ✕ | ✕ | ✕ | ✓ VRAM overcommit + swap policy |
| Deduplicate base model weights across apps | ✕ | ✕ | ✕ | ✓ Shared weights in VRAM |
| Drop-in compatibility with existing pods/containers | ✓ | ✓ | ✓ | ✓ No code changes |
| Kubernetes-native deployment model | ✓ | ✓ | ✓ | ✓ Operator model |
Other tools schedule between jobs. WoolyAI schedules within the GPU.
Co-Exists With Your Existing ML Platform
Integration Model
Drop-in compatibility with your existing ML platform!
Works with your existing ML containers
Deploy with WoolyAI's Kubernetes GPU Operator or Slurm
See WoolyAI on Your Fleet
Step 1
Step 2
(5 min setup)
Measure headroom -> Review findings -> Plan rollout